The Impact of a Bayesian Penalized Likelihood Reconstruction Algorithm on the Evaluation of Indeterminate Pulmonary Nodules by Dual-Time Point 18F-FDG PET/CT

Bayesian penalized likelihood PET reconstruction impact on quantitative metrics in diffuse large B-cell lymphoma

Evaluate the quantitative, subjective (Deauville score [DS]) and reader agreement differences between standard ordered subset expectation maximization (OSEM) and Bayesian penalized likelihood (BPL) positron emission tomography (PET) reconstruction methods. A retrospective review of 104 F-18 fluorodeoxyglucose PET/computed tomography (CT) exams among 52 patients with diffuse large B-cell lymphoma. An unblinded radiologist moderator reviewed both BPL and OSEM PET/CT exams. Four blinded radiologists then reviewed the annotated cases to provide a visual DS for each annotated lesion. Significant (P < .001) differences in BPL and OSEM PET methods were identified with greater standard uptake value (SUV) maximum and SUV mean for BPL. The DS was altered in 25% of cases when BPL and OSEM were reviewed by the same radiologist. Interobserver DS agreement was higher for OSEM (>1 cm lesion = 0.89 and ≤1 cm lesion = 0.84) compared to BPL (>1 cm lesion = 0.85 and ≤1 cm lesion = 0.81). Among the 4 readers, average intraobserver visual DS agreement between OSEM and BPL was 0.67 for lesions >1cm and 0.4 for lesions ≤1 cm. F-18 Fluorodeoxyglucose PET/CT of diffuse large B-cell lymphoma reconstructed with BPL has higher SUV values, altered DSs and reader agreement when compared to OSEM. This report finds volumetric PET measurements such as metabolic tumor volume to be similar between BPL and OSEM PET reconstructions. Efforts such as adoption of European Association Research Ltd accreditation should be made to harmonize PET data with an aim at balancing the need for harmonization and sensitivity for lesion detection.

Impact of the Bayesian penalized likelihood algorithm (Q.Clear®) in comparison with the OSEM reconstruction on low contrast PET hypoxic images

Purpose

To determine the impact of the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison to OSEM on hypoxia PET/CT images of NSCLC using ¹⁸F-MIZO and ¹⁸F-FAZA.

Materials and methods

Images of low-contrasted (SBR = 3) micro-spheres of Jaszczak phantom were acquired. Twenty patients with lung neoplasia were included. Each patient benefitted from ¹⁸F-MISO and/or ¹⁸F-FAZA PET/CT exams, reconstructed with OSEM and BPL. Lesion was considered as hypoxic if the lesion SUV_max > 1.4. A blind evaluation of lesion detectability and image quality was performed on a set of 78 randomized BPL and OSEM images by 10 nuclear physicians. SUV_max, SUV_mean, and hypoxic volumes using 3 thresholding approaches were measured and compared for each reconstruction.

Results

The phantom and patient datasets showed a significant increase of quantitative parameters using BPL compared to OSEM but had no impact on detectability. The optimal beta parameter determined by the phantom analysis was β350. Regarding patient data, there was no clear trend of image quality improvement using BPL. There was no correlation between SUV_max increase with BPL and either SUV or hypoxic volume from the initial OSEM reconstruction. Hypoxic volume obtained by a SUV > 1.4 thresholding was not impacted by the BPL reconstruction parameter.

Conclusion

BPL allows a significant increase in quantitative parameters and contrast without significantly improving the lesion detectability or image quality. The variation in hypoxic volume by BPL depends on the method used but SUV > 1.4 thresholding seems to be the more robust method, not impacted by the reconstruction method (BPL or OSEM).

Trial registration

ClinicalTrials.gov, NCT02490696. Registered 1 June 2015

Ordered subset expectation maximisation vs Bayesian penalised likelihood reconstruction algorithm in 18F-PSMA-1007 PET/CT

Background

The aim of the study was to compare widely used ordered subset expectation maximisation (OSEM) algorithm with a new Bayesian penalised likelihood (BPL) Q.Clear algorithm in 18F-PSMA-1007 PET/CT.

Methods

We retrospectively assessed 25 18F-PSMA-1007 PET/CT scans with both OSEM and Q.Clear reconstructions available. Each scan was independently reported by two physicians both in OSEM and Q.Clear. SUVmax, SUVmean and tumour-to-background ratio (TBR) of each lesion were measured. Reports were also compared for their final conclusions and the number and localisation of lesions.

Results

In both reconstructions the same 87 lesions were reported. Mean SUVmax, SUVmean and TBR were higher for Q.Clear than OSEM (7.01 vs 6.53 [p = 0.052], 4.16 vs 3.84 [p = 0.036] and 20.2 vs 16.8 [p < 0.00001], respectively). Small lesions (< 10 mm) had statistically significant higher SUVmax, SUVmean and TBR in Q.Clear than OSEM (5.37 vs 4.79 [p = 0.032], 3.08 vs 2.70 [p = 0.04] and 15.5 vs 12.5 [p = 0.00214], respectively). For lesions ≥ 10 mm, no significant differences were observed. Findings with higher tracer avidity (SUVmax ≥ 5) tended to have higher SUVmax, SUVmean and TBR values in Q.Clear (11.6 vs 10.3 [p = 0.00278], 7.0 vs 6.7 [p = 0.077] and 33.9 vs 26.7 [p < 0.00001, respectively). Mean background uptake did not differ significantly between Q.Clear and OSEM (0.42 vs 0.39, p = 0.07).

Conclusions

In 18F-PSMA-1007 PET/CT, Q.Clear SUVs and TBR tend to be higher (regardless of lesion localisation), especially for small and highly avid lesions. Increase in SUVs is also higher for lesions with high tracer uptake. Still, Q.Clear does not affect 18F-PSMA-1007 PET/CT specificity and sensitivity.

Evaluation and Optimization of a New PET Reconstruction Algorithm, Bayesian Penalized Likelihood Reconstruction, for Lung Cancer Assessment According to Lesion Size

OBJECTIVE. The purpose of this study was to characterize the Bayesian penalized likelihood (BPL) reconstruction algorithm in comparison with an ordered subset expectation maximization (OSEM) reconstruction algorithm and to determine its optimal penalization factor (expressed as a beta value) for clinical use. MATERIALS AND METHODS. FDG PET/CT scans of 46 patients with lung cancer were reconstructed using OSEM and BPL with beta values of 200, 300, 400, 500, and 1000. The liver signal-to-noise ratio, mean standardized uptake value (SUV_mean) of the liver, and maximum standardized uptake value (SUV_max) and SUV_mean of the cancers were measured. Tumors were categorized into three size groups, and the percentage difference in the tumor SUV_max between OSEM and BPL with a beta value of 200 as well as the percentage difference in the SUV_max between BPL with a beta value of 200 and BPL with a beta value of 1000 were calculated. Image quality was assessed by visual scoring. RESULTS. BPL showed a significantly higher liver signal-to-noise ratio than OSEM, except for BPL with a beta value of 200. The liver SUV_mean showed no statistical difference among all algorithms. The SUV_max and SUV_mean of tumors decreased as the beta value increased. BPL with a beta value of 200 produced a significantly higher tumor SUV_max than did OSEM (p < 0.01), and BPL with a beta value of 400, 500, or 1000 produced a significantly lower tumor SUV_max than did OSEM (p < 0.01). Visual analysis showed the highest and lowest scores for BPL with beta values of 500 and 200, respectively. In the small size group, the percentage difference in the SUV_max between OSEM and BPL with a beta value of 200 and the percentage difference in the SUV_max between BPL with a beta value of 200 and BPL with a beta value of 1000 were significantly larger than that in the other size groups (p < 0.01). CONCLUSION. The BPL algorithm improves image quality without compromising image quantification. A beta value of 500 appeared to be optimal in this study. Smaller tumors were more influenced by BPL.

Impact of PET reconstruction protocols on quantification of lesions that fulfil the PERCIST lesion inclusion criteria

BACKGROUND

The aim of this study was to compare liver and oncologic lesion standardized uptake values (SUV) obtained through two different reconstruction protocols, GE's newest clinical lesion detection protocol (Q.Clear) and the EANM Research Ltd (EARL) harmonization protocol, and to assess the clinical relevance of potential differences and possible implications for daily clinical practice using the PERCIST lesional inclusion criteria. NEMA phantom recovery coefficients (RC) and SUV normalized for lean body mass (LBM), referred to as SUV normalized for LBM (SUL), of liver and lesion volumes of interest were compared between the two reconstruction protocols. Head-to-toe PET/CT examinations and raw data from 64 patients were retrospectively retrieved. PET image reconstruction was carried out twice: once optimized for quantification, complying with EARL accreditation requirements, and once optimized for lesion detection, according to GE's Q.Clear reconstruction settings.

RESULTS

The two reconstruction protocols showed different NEMA phantom RC values for different sphere sizes. Q.Clear values were always highest and exceeded the EARL accreditation maximum for smaller spheres. Comparison of liver SUL_mean showed a statistically significant but clinically irrelevant difference between both protocols. Comparison of lesion SUL_peak and SUL_max showed a statistically significant, and clinically relevant, difference of 1.64 and 4.57, respectively.

CONCLUSIONS

For treatment response assessment using PERCIST criteria, the harmonization reconstruction protocol should be used as the lesion detection reconstruction protocol using resolution recovery systematically overestimates true SUL values.

BSREM Reconstruction for Improved Detection of In-Transit Metastases With Digital FDG-PET/CT in Patients With Malignant Melanoma

Block sequential regularized expectation maximization (BSREM) is a Bayesian penalized-likelihood reconstruction algorithm for PET, which reaches full convergence without the detriment of deteriorating the image quality by noise. Therefore, BSREM might have implications particularly for the detection of small lesions, which may be beneficial in melanoma patients. Our case of a 70-year-old man with metastasized malignant melanoma illustrates the impact of such a novel iterative PET reconstruction algorithm. Whereas the lymph node metastases are seen with the latest generation ordered subset expectation maximization reconstruction, the in-transit metastases are identified straightforward only with BSREM reconstruction.

Effect of Time-of-Flight and Regularized Reconstructions on Quantitative Measurements and Qualitative Assessments in Newly Diagnosed Prostate Cancer With 18F-Fluorocholine Dual Time Point PET/MRI

Recent technical advances in positron emission tomography/magnetic resonance imaging (PET/MRI) technology allow much improved time-of-flight (TOF) and regularized iterative PET reconstruction regularized iterative reconstruction (RIR) algorithms. We evaluated the effect of TOF and RIR on standardized uptake values (maximum and peak SUV [SUVmax and SUVpeak]) and their metabolic tumor volume dependencies and visual image quality for 18F-fluorocholine PET/MRI in patients with newly diagnosed prostate cancer. Fourteen patients were administered with 3 MBq/kg of 18F-fluorocholine and scanned dynamically for 30 minutes. Positron emission tomography images were divided to early and late time points (1-6 minutes summed and 7-30 minutes summed). The values of the different SUVs were documented for dominant PET-avid lesions, and metabolic tumor volume was estimated using a 50% isocontour and SUV threshold of 2.5. Image quality was assessed via visual acuity scoring (VAS). We found that incorporation of TOF or RIR increased lesion SUVs. The lesion to background ratio was not improved by TOF reconstruction, while RIR improved the lesion to background ratio significantly ( P < .05). The values of the different VAS were all significantly higher ( P < .05) for RIR images over TOF, RIR over non-TOF, and TOF over non-TOF. In conclusion, our data indicate that TOF or RIR should be incorporated into current protocols when available.